End-to-end learning

Field

Artificial Intelligence, Machine Learning, Deep Learning

Advantages

Streamlined learning, enhanced adaptability, more accurate and efficient models, simplifies AI system design, learns most relevant features for the task

Definition

A machine learning method where a single model is trained to map raw inputs to desired outputs, learning to extract useful features without manual feature engineering or intermediate steps.

Limitations

Requires large amounts of labeled data, can be more difficult to interpret and debug than models with explicit intermediate steps

Core Principle

Seamless integration of data-driven learning and decision-making processes

Key Components

Neural networks, convolutional neural networks, recurrent neural networks, transformers

Paradigm Shift From

Conventional machine learning models relying on manual feature selection and engineering

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End-to-end learning is a fundamental concept in the field of AI, particularly in machine learning. It refers to a method where a system learns to perform a specific task without relying on intermediate stages of processing or feature extraction. In essence, the system takes raw data as input and directly produces meaningful outputs without the need for manual feature engineering. This approach allows for streamlined learning and decision-making processes within AI systems. [...] The core principle of end-to-end learning revolves around the seamless integration of data-driven learning and decision-making processes. Unlike traditional approaches, which involve handcrafted feature engineering and sequential model building, end-to-end learning directly learns from raw data and autonomously refines the underlying representations to optimize task performance. This approach is particularly prevalent in the domain of deep learning, where neural network architectures are [...] In conclusion, the concept of end-to-end learning represents a pivotal paradigm shift in the development of AI systems, offering streamlined learning processes and enhanced adaptability across diverse applications. As the technology continues to evolve, end-to-end learning is poised to play a central role in shaping the future landscape of artificial intelligence, fostering innovation and driving advancements across various industries. Lark, bringing it all together

In end-to-end learning, a model is trained to map raw inputs to desired outputs using a large amount of labeled data. The model learns to extract useful features from the data and to use these features to make predictions. This is typically done using deep learning techniques, such as convolutional neural networks or recurrent neural networks. End-to-end learning can simplify the design of AI systems, as it removes the need for manual feature engineering and intermediate steps. [...] End-to-end learning in AI refers to training a single model to perform a task from raw input to final output, without any intermediate steps or feature engineering. This approach has been successful in many areas of AI, such as speech recognition and machine translation, where end-to-end models have achieved state-of-the-art results. ## How does end-to-end learning work? [...] ## What are the advantages and limitations of end-to-end learning? End-to-end learning can lead to more accurate and efficient models, as the model can learn to extract the most relevant features for the task. However, it also requires large amounts of labeled data, and can be more difficult to interpret and debug than models with explicit intermediate steps. Back Go Social with Us © 2025 by TEDAI San Francisco Contact Privacy Glossary Go Social with Us © 2025 by TEDAI San Francisco

At its core, End-to-End Learning within the AI context signifies a transformative approach, enabling AI models to learn directly from raw data. This methodology not only marks a departure from traditional feature extraction and engineering steps but also heralds a new era of models that are both more adaptable and powerful. According to a snippet from Lark, this innovation has broad implications across diverse fields including computer vision, natural language processing (NLP), and autonomous [...] The convergence of these methodological and technological advancements has catapulted end-to-end learning to the forefront of AI research and application. By allowing models to learn directly from raw data, end-to-end learning not only simplifies the model development process but also opens up new possibilities for AI systems that are more adaptable, efficient, and capable of tackling complex, real-world tasks. Image 34 [...] End-to-End Learning in AI represents a paradigm shift from the conventional machine learning models which rely heavily on manual feature selection and engineering. Traditional methods necessitate a meticulous, often subjective process of identifying which aspects of the data are relevant before the model can learn to make predictions. In stark contrast, end-to-end learning streamlines this process by:

The aim of End-to-End learning is to train each layer’s weights of a multilayer fully connected network concurrently. The output of the multilayer NN can be formulated as:\begin{equation} \boldsymbol {y}_{NN}(k)=\boldsymbol {\sigma }\left ({\mathbf {W}_{n}\boldsymbol {\phi }_{n-1}(\cdots \mathbf {W}_{2}\boldsymbol {\phi }_{1}(\mathbf {W}_{1}\boldsymbol {x}_{1}(k)))}\right ) \tag{1}\end{equation}View SourceImage 6: Right-click on figure for MathML and additional features.\begin{equation} [...] A forward simultaneous End-to-End learning method, where an n layer multi-layer fully connected network is updated concurrently with n-1 virtual learning systems. (a) Full network- a deep fully connected network with n-1 hidden layers. (b) n-1 virtual learning systems. The input of each learning system {x}_{j} can be calculated by passing the original input through previous layers’ weights. For each virtual learning system, only the input weights \hat {\mathrm { W}}_{j} are kept for the full [...] Image 16: FIGURE 1. - A forward simultaneous End-to-End learning method, where an $n$ layer multi-layer fully connected network is updated concurrently with $n-1$ virtual learning systems. (a) Full network- a deep fully connected network with $n-1$ hidden layers. (b) $n-1$ virtual learning systems. The input of each learning system ${x}_{j}$ can be calculated by passing the original input through previous layers’ weights. For each virtual learning system, only the input weights $\hat {\mathrm {